SDP: Spectral-Decomposed Prompting for Continual Learning

Continual Learning (CL) enables models to sequentially acquire new knowledge while retaining previous knowledge. However, the challenge of catastrophic forgetting arises when new tasks interfere with previously acquired knowledge. Prompt-based approaches, leveraging pre-trained models, show promise in adapting to new tasks and reducing the risk of overfitting while mitigating catastrophic forgetting. However, existing approaches operate primarily in the spatial domain, neglecting the spectral entanglement between style-biased amplitude components and semantics-preserving phase components in feature representations. In this work, we propose the Spectral-Decomposed Prompting (SDP) method, a novel prompt-based approach that dynamically generates prompts based on the current input using a spectral decomposition strategy. By employing the Fast Fourier Transform (FFT), the query feature and the token embedding are transformed and decomposed into amplitude and phase spectra. SDP suppresses style-sensitive amplitude variations via spectral normalization while adaptively modulating phase components through task-aware attention mechanisms. It minimizes the disturbance of stylistic variations and enhances the semantic representations learning for prompts. Extensive experiments demonstrate that SDP significantly improves adaptability and performance in continual learning tasks, outperforming state-of-the-art methods while mitigating catastrophic forgetting.